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Our latest paper on AI-augmented epidemiology from Google Cloud published in Nature Digital Medicine & Google AI Blog! Our models have been used in US & Japan for creating COVID-19 testing targets, allocating resources and simulating policies.

Beyond covid-19, how could mRNA-based vaccines deal with diseases such as malaria, TB and HIV? The founders of BioNTech SE explain econ.st/3oer1kw

Our recent work: Temporal Fusion Transformer (TFT), for interpretable time series forecasting. 📈 TFT has been used to help retail and logistics companies for accurate and interpretable demand forecasting, and for applications related to climate change.

Our new blog post on integrating rules into deep learning towards interpretable, robust and reliable deep neural networks…

Our recent work introduces a novel way of designing attention based visual understanding architectures, demonstrating high accuracy, useful interpretability capabilities and learning benefits. See our paper for more details: arxiv.org/pdf/2105.12723…

Grateful for the opportunity to have collaborated with Google AI Tomas Pfister Sercan Arık and others on the Google-Harvard Covid-19 Public Forecast model that was part of this broader ensemble modeling effort. Excited to this PNASNews paper out. pnas.org/doi/10.1073/pn…

"Algorithmic fairness in pandemic forecasting: lessons from COVID-19" - we present our perspectives for equitable ML innovations based on our experience building pandemic forecasting models, with brilliant collaborators from Harvard led by Thomas Tsai, MD, MPH nature.com/articles/s4174…

Self supervised learning is revolutionizing AI using large unlabeled datasets. We show that maximizing mutual information between alternative representations of the same input is a practical method for self supervised learning that is immune to the dreaded collapse problem.

Our paper (arxiv.org/abs/2209.07999) just got accepted to NeurIPS Conference 2022! Congratulations to Alper Erdogan for coming up with the idea and Serdar Ozsoy, Shadi Hamdan, Sercan Arık for a great collaboration.

Privacy concerns can arise as a key bottleneck for data sharing, especially for sensitive domains like healthcare. We propose a novel generative modeling framework, that yields privacy-preserving synthetic EHR data with high fidelity.

At #GoogleIO, we unveiled four important ways we’re supporting customers and partners to leverage generative AI capabilities. cloud.google.com/blog/products/…

Our recent work on using LLMs to query databases using natural language:

Although we have witnessed several breakthroughs by transformers (especially LLMs), simple MLPs (or mixers in a fancier name 🙂) can be competitive or even better on time-series forecasting problems 🚀 Feel free to give a try on your task! Code: github.com/google-researc…

Prompting can be very important to get accurate outputs with LLMs but challenging for humans - handcrafting even a small number of demos can be difficult or, for unseen tasks, impossible. Our recent work on automatic prompting addresses this...

Google's new ASPIRE system teaches AI the value of saying 'I don't know' venturebeat.com/ai/googles-new…

Selective prediction can improve reliability of LLMs by allowing them to abstain from making predictions when they are unsure. Our novel framework, ASPIRE, is based on adaptation with self evaluation to push state-of-the-art in selective prediction. For details:

Multimodal LLMs (MLLMs) excel at many tasks, but object hallucination (generated descriptions of non-existent objects) hinders widespread use. Learn about a method to reduce hallucinations in existing MLLMs and maintain their vision-language capabilities →